GB2565269A

GB2565269A - Relay operations in a cellular network

Info

Publication number: GB2565269A
Application number: GB1710498.5A
Authority: GB
Inventors: Eddine Hajri Salah; Assaad Mohamad
Original assignee: CentraleSupelec; TCL Communication Ltd
Current assignee: CentraleSupelec; TCL Communication Ltd
Priority date: 2017-06-30
Filing date: 2017-06-30
Publication date: 2019-02-13
Also published as: CN110800359A; CN110800359B; GB201710498D0; WO2019001039A1

Abstract

The invention relates to a relaying method for a cellular communication system. A base station groups a plurality of UEs (500) into a cluster of relay nodes, based on their relative locations. For example, proximal or “co-located” UEs, which lie within an area of a predetermined radius, may be grouped together to form a cluster. The cluster of UEs are scheduled as relay nodes in the cellular communication system. A base station may form multiple clusters. Each cluster of relay nodes may support communications between the base station and a target UE 800 or multiple target UEs 800. The clustered UEs act as a virtual MIMO base station. In a preferred embodiment communications are performed based on first and second link quality indications. The first link quality indication relates to the link between the cluster of relay nodes 800 and the target UE 500 and the second link quality indication relates to the link between the cluster of relay nodes and the base station.

Description

Relay Operations In A Cellular Network

Technical Field [1] The present disclosure generally relates to wireless communications, and specifically to relay operations in a cellular network.

Background [2] Current telecommunications networks operate using radio spectrum in which multiple accesses to the communications resources of the radio spectrum is strictly controlled. Each User Equipment (UE) connected to a network is provided a “slice” of the spectrum using a variety of multiple access techniques such as, by way of example only but not limited to, Frequency Division Multiplexing (FDM), Time Division Multiplexing (TDM), Code Division Multiplexing (CDM), and Space Division Multiplexing (SDM) or a combination of one or more of these techniques. Even with a combination of these techniques, with the popularity of mobile telecommunications, the capacity of current and future telecommunications networks is may be limiting.

[3] 5G New Radio (5G/NR) is the name chosen by the Third Generation Partnership Project defining the global 5G telecommunications standard for the specification of a new 5G wireless air interface. 3G and 4G communications standards such as current Long Term Evolution (LTE)/LTE advanced standards were directed to connecting people. Instead, 5G/NR is, at least in part, intended to connect everything and provide a unifying connectivity fabric. 5G/NR may bring about a suite of families such as enhanced Mobile Broadband, massive Machine Type Communications, and Ultra-Reliable and Low Latency Communications (URLLC). URLLC is defined as one of the key target scenarios to be supported by 5G/NR and should provide low latency communications and high reliability (e.g. URLLC reliability requirement for one transmission of a packet is 1 -1 O'5 6 for X bytes (e.g., 20 bytes) with a user plane latency of 1ms) and high reliability.

[4] The high demand in data traffic coupled with the ever increasing number of connected devices and the emergence of Internet of Things (loT) puts a heavy burden on telecommunications networks.

[5] Thus, there is a desire and a need for a mechanism that further improves the capacity of wireless communications networks.

Summary [6] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[7] According to an aspect, there is provided a method performed by a base station for handling communication of user equipments (UE) in a telecommunications network. The base station groups a UE with another UE into a group of relay nodes based on location of the UE relative the other UE. The base station then schedules the group of relay nodes as relay nodes in the telecommunications network.

[8] The base station may optionally schedule a different UE to communicate with the UE as the relay node.

[9] The base station may optionally schedule a maximum number of different UEs to communicate with each group of UEs. The scheduling is proximity based and may depend on a maximum distance from a center of the group to the different UE.

[10] The base station may optionally receive an indication of link quality of a link between the group of relay nodes and the different UE. Furthermore, the base station may receive an indication of link quality of a link between the group of relay nodes and the base station; and may perform communication within the telecommunications network based on the received indications.

[11] Optionally, the location of the UE relative the other UE is within an area of a maximum radius.

[12] The object of the invention is to provide a mechanism that improves the capacity of wireless communications networks. This is achieved by scheduling the relay nodes according to a location criterion that enables to provide a good wireless back-haul link between the base station and the relay nodes.

[13] According to further aspects of the invention there is provided a base station apparatus including a processor unit, a storage unit and a communications interface, where the processor unit, storage unit, and communications interface are configured to perform the method(s) as described or as described herein.

[14] The methods described herein may be performed by software in machine readable form on a tangible storage medium or computer readable medium e.g. in the form of a computer program comprising computer program code means adapted to perform all the steps of any of the methods described herein when the program is run on a computing device or base station and where the computer program may be embodied on a computer readable medium. Examples of tangible (or non-transitory) storage media include disks, thumb drives, memory cards etc. and do not include propagated signals. The software can be suitable for execution on a parallel processor or a serial processor such that the method steps may be carried out in any suitable order, or simultaneously. For example, another other aspect of the invention there is provided a computer readable medium comprising a computer program, program code or instructions stored thereon, which when executed on a processor, causes the processor to perform a method for handling communication of UEs as described herein.

[15] In a further aspect of the invention there is provided a computer readable medium comprising a computer program, program code or instructions stored thereon, which when executed on a processor, causes the processor to perform a method for handling communication of UEs as described herein.

[16] This acknowledges that firmware and software can be valuable, separately tradable commodities. It is intended to encompass software, which runs on or controls “dumb” or standard hardware, to carry out the desired functions. It is also intended to encompass software which “describes” or defines the configuration of hardware, such as HDL (hardware description language) software, as is used for designing silicon chips, or for configuring universal programmable chips, to carry out desired functions.

[17] The preferred features may be combined as appropriate, as would be apparent to a skilled person, and may be combined with any of the aspects of the invention.

[18] A benefit of the claimed solution is that it enables to provide service to a wide area without requiring prior network infrastructure deployment. This is very advantageous for network operators since, using the proposed solution, more UEs may be serviced with the same available resources. Another major benefit of the solution is energy efficiency. Using mobile UEs to mimic a base station means that the cumbersome energy cost of deployed infrastructure is reduced and so is the needed transmission power.

Brief Description of the Drawings [19] Further details, aspects and embodiments will be described, by way of example only, with reference to the drawings. Elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. Like reference numerals have been included in the respective drawings to ease understanding.

[20] Figure 1 is a schematic overview of a telecommunications network according to embodiments herein; [21] Figure 2 is a schematic overview of a telecommunications network according to embodiments herein; [22] Figure 3 is a signaling scheme according to embodiments herein; [23] Figure 4 is a block diagram depicting a time slot according to embodiments herein; [24] Figure 5 is a schematic overview depicting grouping of UEs according to embodiments herein; [25] Figure 6 is a schematic overview depicting grouping of UEs according to embodiments herein; [26] Figure 7 is a simplified flow chart illustrating an exemplary method performed by the base station; [27] Figure 8 shows a comparison of CDFs; [28] Figure 9 shows a comparison of energy efficiency; and [29] Figures 10A-10B are simplified block diagrams illustrating embodiments of a base station.

[30] Detailed Description [31] Fig. 1 is a schematic overview depicting a telecommunications network according to embodiments herein.

[32] Those skilled in the art will recognize and appreciate that the specifics of the examples described are merely illustrative of some embodiments and that the teachings set forth herein are applicable in a variety of alternative settings.

[33] Referring now to FIG. 1, an example of part of an NR cellular communication system operating in accordance with embodiments of the invention is illustrated and indicated generally at 100 and comprises a base station 101 such as an evolved Node B (eNB) supporting a cell. The base station 101 may support a multiplicity of cells.

[34] Telecommunications network 100 may comprise or represent any one or more communication network(s) used for communications between User Equipment (UE) 500 and 800 and other devices, content sources or servers that are connected to the telecommunications network 100. The telecommunication network 100 may also comprise or represent any one or more communication network(s), one or more network nodes, entities, elements, application servers, servers, base stations or other network devices that are linked, coupled or connected to form the telecommunications network 100. The coupling or links between network nodes may be wired or wireless (for example, radio communications links, optical fibre, etc.). The telecommunication network 100 may include any suitable combination of core network(s) and radio access network(s) including network nodes or entities, base stations, access points, etc. that enable communications between the UEs, network node 101 of the telecommunication network 100, content sources and/or other devices connecting to the telecommunication network 100.

[35] Examples of telecommunication network 100 that may be used in certain embodiments of the described apparatus, methods and systems may be at least one communication network or combination thereof including, but not limited to, one or more wired and/or wireless telecommunication network(s), one or more core network(s), one or more radio access network(s), one or more computer networks, one or more data communication network(s), the Internet, the telephone network, wireless network(s) such as the WiMAX, WLAN(s) based on, by way of example only, the IEEE 802.11 standards and/or Wi-Fi networks, or Internet Protocol (IP) networks, packet-switched networks or enhanced packet switched networks, IP Multimedia Subsystem (IMS) networks, or communications networks based on wireless, cellular or satellite technologies such as mobile networks, Global System for Mobile Communications (GSM), GPRS networks, Wideband Code Division Multiple Access (W-CDMA), CDMA2000 or Long Term Evolution (LTE)/LTE Advanced networks or any 2nd, 3rd, 4th or 5th Generation and beyond type communication networks and the like.

[36] A user equipment may be referred to as a wireless device such as a wireless communication terminal, communication equipment, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or user equipment e.g. smart phone, laptop, mobile phone, sensor, camera, relay, mobile tablets. The base station may be referred to a network node, an access point such as a wireless local area network (WLAN) access point, an access controller, a radio base station such as a NodeB, an evolved Node B (eNB, eNodeB), a base transceiver station or similar.

[37] It is herein assumed a single cell massive Multiple Input Multiple Output (ΜΙΜΟ) system in Time Division Duplexing (TDD) mode with pilot based uplink channel estimation. K single antenna UEs are present in the coverage area, and a long range base station 101 has M antennas. At each slot the base station 101 is allowed to estimate the channel of at most τ UEs using orthogonal pilot sequences with length τ . In classical massive ΜΙΜΟ systems, the maximum number of scheduled UEs per slot is limited by the length of the training period.

Scheduling more UEs for uplink requires increasing τ which, consequently, reduces the available resources for data transmission in each time slot, and hence reduces the achievable throughput per UE.

[38] Embodiments herein aim to increase the number of scheduled UEs without increasing the maximum training length τ. An option is the deployment of more base stations but this is costly for network operators. Backhaul linked relay systems provide a possible route to improving coverage and capacity, and the current disclosure proposes an intelligent relay selection scheme. A massive ΜΙΜΟ base station is used to provide a low cost and efficient access network that does not need more infrastructure to be deployed.

[39] A large number of mobile UEs are selected as relay nodes to provide two layers of massive ΜΙΜΟ. By scheduling these relay nodes in an optimized manner, the telecommunications network is transformed to a combination of virtual massive ΜΙΜΟ cells connected to the core network with a wireless back-haul through a link with the long range massive ΜΙΜΟ base station.

[40] Previous relay based solutions required the deployment of a large number of access points with costly back-haul links. In the current disclosure, no additional signalling is needed among the relay nodes since relay selection and UE scheduling is performed by the base station 101. The relay clusters can be established using existing LTE and 5G control plans, once the disclosure herein has identified appropriate UEs and relay clusters.

[41] The method proposed in this invention enables more UEs to be scheduled in the system without requiring any additional resources for training. It also allows the coverage area of the network to be increased with the existing infrastructure while reducing, at the same time, the energy consumption of the telecommunications network. The advantages may be achieved through selecting a number of co-located mobile UEs to act as relay nodes according to an optimized framework. The basic concept of the invention is depicted in Fig. 2.

[42] Fig. 3 is a signalling scheme according to embodiments herein. Assuming a coherence time slot of duration Ts, in a conventional TDD protocol, this time slot is divided between uplink channel estimation, and data transmission in the uplink (UL) and downlink (DL). The uplink channel estimation is done using orthogonal training sequences of length τ . This means that, at each time slot, at maximum τ UEs can be scheduled for training when using a conventional TDD protocol. This limitation in the training duration also results in reusing the same pilot sequences in different cells giving rise to pilot contamination between cells if the cells cannot be sufficient separated. Scheduling more UEs in conventional massive MIMA ΤΓΊΓ) ewctomc rom liroc an inrroaco in T which rodiiroc tho nart nf oarh rnhoronro interval that is dedicated for data transmission. The current disclosure aims to overcome this limitation by using some of the mobile UEs as relay nodes in order to mimic massive ΜΙΜΟ base stations without requiring any costly back-haul link or infrastructure. The main concept is to use co-located UEs in the covered area as relay nodes. These relay nodes will be organised in groups based on their location. The scheduled relay nodes may be considered as the antennas of a virtual Massive ΜΙΜΟ base station. Practically, the proposed invention can be compared to a massive ΜΙΜΟ system with a wireless back-haul.

[43] Referring to Figure 3, at step 301 the base station 101 groups the UEs 500 into groups based on their location relative one another, in particular the base station 101 determines UEs that are considered as co-located. The UEs or relay nodes 500, in each group of UEs provide an array gain comparable to that of a classical massive ΜΙΜΟ base station. Communication among the relays may not be necessary, regardless of their large number, due to the coordination by the base station. This means that signalling overhead is reduced and should not act to limit system capacity. In this example, only co-located UEs are selected as relay nodes. This means that the UEs from each group will have comparable second order channel statistics and hence their channel covariance matrices are concentrated in the same signal subspace. This condition is set in order to manage the level of inter-relay interference. If another time period L is assumed and during this period, it may be assumed that the second order statistics of the channel which depend on user locations, are constant. At the beginning of each interval L , the base station 101 may use a graph optimization framework in order to optimally identify groups of co-located UEs in the network to be scheduled as relay nodes. The relay selection optimization problem will identify Nr eligible relay groups. Practically, this invention divides the area into small cells serviced each by a cluster of relay nodes densely distributed in its centre.

[44] At step 302 the base station 101 then schedules the UEs 500 that are considered co-located as relay nodes in the telecommunications network. Each coherence slot is divided into 3 major parts as shown in Figure 4. Note that these parts do not have to be performed following the order given in Figure 4. Any other order can be used, provided they are duplexed in time. One part is dedicated for training, which starts with Uplink training between the relay node and the long range base station 101, followed by Uplink training between the scheduled UEs 800 and the relay nodes 500. The two other parts of the interval will be dedicated, respectively, for uplink and downlink data transmission. Note that the transmission on the link R-U between the relay nodes 500 and the scheduled UEs 800 and on the link B-R between the base station 101 and the relay nodes 500 will be separated in time. This is done in order to avoid self-interference at the relay level which can be very problematic due to the small distances between the relay nodes. The periods of time dedicated to each link will be computed by the base station 101 with respective proportions 1-/ and γ. γ will depend on the bottleneck of the system, meaning the link with the lowest rate. The users within each relay group will be scheduled for uplink training at each time slot. Since these UEs are clustered geographically, their channel covariance matrices may be spanned by the same signal space eigenvectors. The graph optimization framework will also minimize the difference between the channel covariance matrices of the relay nodes 500 within a given group. This allows to considerably reduce the interference between groups and enables a reuse the same pilot sequences among the groups.

[45] At step 303 the base station 101 may further schedule other UEs 800 connected to the relay nodes.

[46] At step 304 the base station 101 may then determine channel estimates for a link between the base station 101 and the group of UEs. During the uplink training phase between the relay nodes 500 and the base station 101, the received training signal at the base station 101 may be written as: [47]

[48] Here, gf represents the wireless channel between the base station 101 and relay node i in group or cluster k. qt. e CTXl, z = 1../ denotes the uplink pilot sequences used by the relay nodes (g/q} = St]) and N^s denotes a white Gaussian noise vector. In order to decode the signal of each relay i,k , the base station 101 can use a matched filter, zero forcing or MMSE receiver and an estimate of its channel. Once the relay channel estimated, the remaining UEs in the network will associate with the nearest relay group in order to start transmission. Each group is allowed to communicate at most with /r UEs. Note that in the proposed invention, only relay nodes are allowed to communicate with the backhaul linked base station 101. This enables to reduce the transmission power used in both uplink and downlink since the communication distances are reduced.

[49] At step 305 the base station 101 may further determine channel estimate of a link between the group of UEs, i.e. the group of relay nodes, and the UE 800. For example, UEs 800 scheduled for transmission may send their pilot sequences. The received training signal at the relay node k in relay group r may be given by:

[50]

{54] Here gy represents the wireless channel between the relay node k,r and the UE i associated with group j. p.^Q,' ,z = l..rr denotes the uplink pilot sequences used by the scheduled UEs for data transmission (p/= By) and denotes an additive white

Gaussian noise vector at relay node k,r . The channels gE between each UE I and each relay node k from its serving group r will be estimated individually by the relay node. This can be done for example using MMSE or any other estimation method.

[52] At step 306 once all channel estimates are performed such as Channel State information (CSI) estimates, data transmission will start. In order to decode the uplink data signal coming from the scheduled UE l,r, each relay nodek,r may apply, independently, the conjugate of its locally obtained channel estimate gE . Each relay node from group r may then send the decoded signal to the base station 101. At this stage, the base station 101 may combine the received signal from all relay nodes in the group and the achievable rate Rlr of the Ith UE communicating with the r"‘ relay cluster may then be: [53] [54] [55] [56] Here Rrmjn is the minimum rate provided by group r, I? is the interference due to pilot contamination and I"r represents the impact of the rest of the interference plus channel estimation error and noise. When the maximum number of relays per group τ increases, I"r decreases with a rate proportional to the variance of the relays large scale fading coefficients. Consequently, the proposed location based relay selection method results in reducing interference.

[57] During the downlink phase on link B-R, the base station 101 may use the conjugate of the estimates of the channel between the base station 101 and the different

groups in order to precode the data. The received downlink data signal at the relay node k group r may be given by: [58]

[59] Where, dSJ denotes the data symbol intended to relay node 5 in group j and /V^J denotes an additive white Gaussian noise coefficient. During the downlink phase on link R— U, the relay node k in group r may precode the data signal using the conjugate of the locally obtained channel estimate gE to every i,i = 1..rr connected with group r. The received downlink data signal at UE /, communicating with group r may be given by: [60]

[61] Where, di} denotes the data symbol intended to the ith UE communicating with group j, and denotes an additive white Gaussian noise coefficient. The number of the established groups, denoted Nr, will be derived by the graph optimization problem. It depends on the maximum allowed distance between the relay nodes denoted by Rmax. Rmax is a design parameter that depend on UE density. The value of Rmax may satisfy the following condition: [62]

[63] λά refers to the spatial density of UEs 500 in the covered area. The condition guarantees the existence of the required number of relay nodes per group, on average. In the presence of a large number of connected UEs, Rmax can be lowered resulting in denser distribution of the relays in the groups which improves the achievable throughput. This is quite promising for urban areas, where the occurrence of a concentration of connected UEs in a restricted area is highly probable, specially in loT applications. This means that we can actually leverage the high demand in order to provide more throughput and schedule more UEs. Another design parameter in the proposed solution is the maximum distance between a UE 800 and its serving group dmax. dmax also depends on UE density and its value may verify the following condition:

[64]

[65] This condition guarantees the scheduling of the desired number of UEs, on average. Compared with a classical massive ΜΙΜΟ system in TDD mode, the proposed solution enables to schedule more users, achieve higher throughput, increase coverage and reduce the energy and infrastructure requirements of the network.

[66] Figure 7 illustrates exemplifying methods performed by the base station 101. According to the various embodiments herein, one or more of the following steps may be performed as applicable. The same or similar reference numerals have been used to denote the same or similar steps, or actions. The method may use as inputs: UEs position in the covered area. A maximum training duration for relay nodes per slot denoted τ. k maximum duration per slot dedicated to training between the relay nodes and the scheduled UEs 800 denoted τ,.. A maximum radius Rmax. A maximum distance between a UE 800 and its serving group dmax. The output may be: establishing a link between the base station, Nr groups, and all UEs 800 scheduled to communicate with the groups.

[67] The full clustered massive ΜΙΜΟ relay method, which is executed at the beginning of each period L , may follow: [68] At Step 701 the base station groups a UE 500 and another UE 500 into a group of relay nodes based on location of the UE relative the other UE. The location of the UE relative the other UE may be within an area of a maximum radius. Thus, the base station 101 starts by establishing the groups of UEs. In this invention, relay nodes from each group may need to be located within the area with the maximum radius Rmax. In order to select the UEs of the group, the base station 101 starts by clustering or grouping the covered UEs according to their location. Note that the number of groups will depend on Rmax. Then the UEs selected as relay nodes will be grouped in Nr groups containing each, at maximum τ UE from each location based group. Each group will relay the signal of the scheduled UEs 800 for data transmission within its neighbourhood. A UE 800 may be scheduled for data transmission if its covered by either the base station 101 or one of the relay nodes. Note that these groups are not required to contain the same number of scheduled UEs. This will be decided by the maximum allowed distance between the relay group centre and the scheduled UE 800 denoted by dmax. Since classical clustering algorithms cannot provide the required user grouping, it is herein developed an optimized relay selection method based on two consecutive graph problems. The following clustering method may be applied in this step:

[69] The base station 101 may start by constructing a location based graph G(V,E) where each vertex v e V represents a UE as pictured in figure 6. An edge e(v,w) between UE u and v is inserted whenever the distance between the two UEs is lower or equal to Rmax . Note that the resulting graph is actually an interval graph. This reduces the complexity of finding the optimal relay user grouping.

[70] Once the graph G(V,E) is constructed, the base station 101 may solve a Cardinality Constrained Graph Partitioning into Cliques with Submodular Cost optimization problem. We denote this problem by (Cl). In graph theory, a clique is a subset of vertices of an undirected graph such that its induced subgraph is complete. Formally, the problem is to find a partition of the graph G(V,E) into cliques Kl,...,KN with a maximum of τ UEs per clique such that the cost function f is minimized. The considered submodular cost function ma be given by: [71]

[72] Where Cui denotes the covariance matrix of the channel between relay node u in cluster i and the Massive ΜΙΜΟ base station. Problem (Cl) can be solved in polynomial time for interval graphs whereas it is NP-hard for general graphs.

[73] A second graph G'(V',E') may then be constructed. Each vertex ν' inV represents one of the cliques that resulted from problem (Cl). Each clique represents a cluster of relay nodes. An edge e'(v',u') between clusters u' and v' is inserted whenever the distance between the two clusters is greater or equal to 2dmax. This will result in yet another interval graph. The base station 101 may then solve a maximum clique problem on C'(C',C'). We denote this problem by (C2). The maximum clique problem search for a clique of maximum cardinality in G'(V',E') which, practically, results in scheduling the largest number of relay groups with a minimum inter-group distance of 2dmax.

[74] In order to solve the relay establishing problems (Cl) and (C2), the following may be performed by the base station 101:

[75] Solve (Cl) in G(V,E) without the cardinality constraints on the number of UEs per clique. (Cl) can be solved in polynomial time in interval graphs using a dynamic programming approach. This will result in a given number of cliques [76]

[77] Solve problem (Cl) with cardinality constraints in every resulting clique [78] The base station 101 constructsG'(V',E') where, each vertex ν' e V represents one of the cliques that resulted from problem (Cl).

[79] The base station 101 then solve a maximum clique problem (C2) in order to find the clique of maximum cardinality in G'(V',E'). As an example, the base station 101 can apply the following algorithm: [80] Initialize the maximum clique size 5 = 0 and the number of iterations .

[81] For k in range(1, Iter ) do : [82] Select a randomly in [0,1] [83] Set initial clique CL = 0

[84] Set set C = V

[85] While |C| > 0 do

[86] Consider G(C) the subgraph induced by C

[87] Consider degG(C}(u) the degree of u e C with respect to G(C) [88] dmm = min\degG{C) (u)\ueC} [89] dmax = max\degG(G) (u)\ueC} [90] L = \ueC\degGiG)(u)>dmm+a(dmm-dmC)}

[91] Select u at random from L

[92j CL — CL ^11 J

[93] C ~ Nc (w) [94] End While [95] Z = [(v,u ,w) | (v,u,w) e V',(u,v) eE',w e CL, u and v are adjacent to all [96] Vertices in [97] While |Z| > 0 do

[98] Select (v,u,w) e Z

[99] CL = CL^{u,v}\fy} [100] Z = [(v,//, it) | (v,w,w) e V',(u,v) e E',w e CL, u and v are adjacent [101] to all Vertices in CL \w] [102] End While [103] If |CZ|>5 [104] MC£|

[105] CL* =CL

[106] End if [107] End for [108] [109] At step 702 the base station 101 then schedules the group of relay nodes, that is the UE and the other UE, as relay nodes in the telecommunications network.

[110] At step 703 the base station 101 may schedule a different UE 800 to communicate with the UE 500 as the relay node. The scheduling of the different UE may comprise scheduling a maximum number of different UEs to communicate with each group of UEs, wherein the scheduling is proximity based and depends on a maximum distance from a center of the group to the different UE. Thus, the base station 101 may schedule at maximum τ r UEs 800 to communicate with each selected group. This scheduling may be proximity based and may depend on the maximum distance dmax from the relay center to the UE 800.

[111] At step 704 the base station 101 may receive an indication of link quality of a link between the group of relay nodes and the different UE 800. The base station may further receive an indication of link quality of a link between the group of relay nodes and the base station 101. The base station 101 may thus schedule the UEs 800 for uplink training in the two links on separated slots with respective lengths τ and τΓ. The base station 101 may estimate only the channels of the relay nodes during the first part of uplink training. Each relay node may estimate the channels to every scheduled UE 800 communicating with its group. No communication among the relays is necessary. Each relay node will do its processing independently and may forward the resulting signal to the base station 101 after applying a Matched filter receiver.

[112] At step 705 the base station 101 may then perform communication within the telecommunications network based on the received indications. Hence, after acquiring channel state information on both links, the rest of the coherence slot will be dedicated for data transmission in the uplink and downlink separately for the two links.

[113] Steps 703 and 704 may be applied by the base station 101 at the beginning of each coherence slot in the period £ .

[114] The disclosure above provides a protocol that enables massive relay scheduling while reducing the need for expensive infrastructure to maintain the coverage of the network.

It also enables to schedule more UEs with the same resources while increasing the achievable throughput. Another advantage of the proposed solution is it low signaling overhead since no communication among the relay nodes is required. Being based only on slow changing side information i.e. the location of the UEs, the disclosure provides a very practical low cost solution for the network operator.

[115] As an example consider a single cell system where τ = 30 and rr = 20 . All scheduled UEs for data transmission are distributed randomly within 0.8 km from their serving relay group. The long range base station is equipped with M = 100 antennas. Three scenarios are compared; in the first one, the proposed method is used. In the second scenario, all scheduled UEs will be served accordina to the conventional TDD Drotocol for massive ΜΙΜΟ; meaning that all UEs will be served by the base station without going through relays. In the third scenario the same area is covered by Nr small base stations, equipped each with Ms = 30 antennas. In the three systems, the same number of users is scheduled for data transmission. The coherence interval is divided between uplink training and data transmission. We consider γ = 0.5, the time slot duration Ts = lms and the available bandwidth B = 2QMhz.

[116] A power consumption model is utilised, for example based on that disclosed in Emil Bjornson, Luca Sanguinetti, Jakob Hoydis and Merouane Debbah, Designing multi-user ΜΙΜΟ for energy efficiency: When is massive ΜΙΜΟ the answer?, IEEE Wireless Communications and Networking Conference (WCNC), Apr 2014, Istanbul, Turkey. Proceedings of WCNC, 2014. The considered model takes into consideration the architectural, transceiver chain and Radio Frequency (RF) power consumptions.

[117] Figure 8 shows a comparison of the CDFs of the achievable sum rates in the three scenarios where the same number of users is scheduled for data transmission. A considerable improvement of the achievable sum rate CDF can be seen using the disclosure hereinbefore. This gain is achieved without any infrastructure modification which render the proposed invention a very cost efficient solution for future network generations and specially for loT applications.

[118] Figure 9 shows a comparison of the energy efficiency CDF in the three scenarios. Owing to the absence of per-deployed infrastructure, the disclosure enables considerable improvement in the achievable energy efficiency of the system while increasing its capacity.

[119] Although the above description describes, by way of example only but is not limited to, the use of Orthogonal Frequency-Division Multiple Access (OFDMA), single-carrier and multi-carrier transmitters/receivers based on OFDM and other carrier formats, it is to be appreciated by the skilled person that the following description may be applied, not only to OFDMA, FDMA or SC-FDMA or other OFDM related systems, but also to other communication systems, receivers and transmitters, such as, by way of example only but is not limited to,

Code Division Multiple Access (CDMA) systems, time division multiple access (TDMA) systems, any other Frequency Division Multiple Access (FDMA) systems, or Space Division Multiple Access (SDMA) systems, or any other suitable communication system or combinations thereof.

[120] Figures 10A and 10B illustrate embodiments of the base station 101.

[121] Figure 10A illustrates various components of an exemplary computing-based base station 101 which may be implemented to include the functionality of the base station 101 as disclosed herein.

[122] The computing-based device comprises one or more processors 802 which may be microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the base station in order to perform measurements, receive measurement reports, schedule and/or allocate communication resources as described in the process(es) and method(s) as described herein.

[123] In some examples, for example where a system on a chip architecture is used, the processors 802, or processor unit, may include one or more fixed function blocks (also referred to as accelerators) which implement the methods and/or processes as described herein in hardware (rather than software or firmware).

[124] Platform software and/or computer executable instructions comprising an operating system 804A or any other suitable platform software may be provided at the computing-based device to enable application software to be executed on the base station. Depending on the functionality and capabilities of the computing device and application of the computing device, software and/or computer executable instructions may include functionality to perform the methods of Figure 7.

[125] For example, the computing device may be used to implement the base station and may include software and/or computer executable instructions that may include functionality to perform the methods of Figure 7.

[126] The software and/or computer executable instructions may be provided using any computer-readable media that is accessible by computing based device. Computer-readable media may include, for example, computer storage media such as memory 804 and communications media. Computer storage media, such as memory 804, includes volatile and non-volatile, removable and non-removable media implemented in any method or technology. A data store 804B of the memory 804 is configured for storage of information such as computer readable instructions, data structures, program modules or other data.

[127] Computer storage media may include, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transport mechanism. As defined herein, computer storage media does not include communication media. Although the computer storage media, such as the memory 804, is shown within the computing-based device it will be appreciated that the storage may be distributed or located remotely and accessed via a network or other communication link, e.g. using communication interface 806.

[128] The computing-based device may also optionally or if desired comprises an input/output controller 810 arranged to output display information to a display device 812 which may be separate from or integral to the computing-based device. The display information may provide a graphical user interface. The input/output controller 810 is also arranged to receive and process input from one or more devices, such as a user input device 814, e.g. a mouse or a keyboard. This user input may be used to set scheduling for communication, or for allocating communication resources, or to set which communications resources are of a first type and/or of a second type etc. In an embodiment the display device 812 may also act as the user input device 814 if it is a touch sensitive display device. The input/output controller 810 may also output data to devices other than the display device, e.g. other computing devices via communication interface 806, any other communication interface, or a locally connected printing device/computing devices etc.

[129] Figure 10B illustrates a schematic block diagram of the base station 101 according to another embodiment. The base station comprises a grouping module 881, a scheduling module 882, and optionally a receiving module 883, and a performing module 884, which are configured to perform the steps performed by the base station 101 according to Figure 7. As will be readily understood by those familiar with communications design, that modules may be implemented using digital logic and/or one or more microcontrollers, microprocessors, or other digital hardware.

[130] The term 'computer' is used herein to refer to any device with processing capability such that it can execute instructions. Those skilled in the art will realise that such processing capabilities are incorporated into many different devices and therefore the term 'computer' or 'computing device' includes PCs, servers, base stations, eNBs, network nodes and other network elements and many other devices.

[131] Those skilled in the art will realise that storage devices utilised to store program instructions can be distributed across a network. For example, a remote computer may store an example of the process described as software. A local or terminal computer may access the remote computer and download a part or all of the software to run the program.

[132] Alternatively, the local computer may download pieces of the software as needed, or execute some software instructions at the local terminal and some at the remote computer (or computer network). Those skilled in the art will also realise that by utilising conventional techniques known to those skilled in the art that all, or a portion of the software instructions may be carried out by a dedicated circuit, such as a DSP, programmable logic array, or the like.

[133] Any range or device value given herein may be extended or altered without losing the effect sought, as will be apparent to the skilled person.

[134] It will be understood that the benefits and advantages described above may relate to one example or embodiment or may relate to several examples or embodiments. The examples or embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages.

[135] Any reference to 'an' item refers to one or more of those items. The term 'comprising' is used herein to mean including the method blocks, features or elements identified, but that such blocks, features or elements do not comprise an exclusive list and a method or apparatus may contain additional blocks, features or elements.

[136] The steps of the methods described herein may be carried out in any suitable order, or simultaneously where appropriate. Additionally, individual blocks may be deleted from any of the methods without departing from the spirit and scope of the subject matter described herein. Aspects of any of the examples described above may be combined with aspects of any of the other examples described to form further examples without losing the effect sought.

[137] It will be understood that the above description of a preferred embodiment is given by way of example only and that various modifications may be made by those skilled in the art. Although various embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of the claims.

Claims

1. A method performed by a base station for handling communication of UEs in a telecommunications network, the method comprising the steps of grouping (701) a UE (500) and another UE into a group of relay nodes based on location of the UE relative the other UE; and scheduling (702) the group of relay nodes as relay nodes in the telecommunications network.

2. The method according to claim 1, further comprising the step of scheduling (703) a different UE (800) to communicate with the UE (500) as the relay node.

3. The method according to claim 2, wherein the scheduling of the different UE comprises scheduling a maximum number of different UEs to communicate with each group of UEs, wherein the scheduling is proximity based and depends on a maximum distance from a centre of the group to the different UE.

4. The method according to claim 2 or claim 3, further comprising the steps of receiving (704) an indication of link quality of a link between the group of relay nodes and the different UE; receiving (704) an indication of link quality of a link between the group of relay nodes and the base station; and performing (705) communication within the telecommunications network based on the received indications.

5. The method according to any of claims 1-4, wherein the location of the UE relative the other UE is within an area of a maximum radius.

6. A computer readable medium comprising program code stored thereon, which when executed on a processor, causes the processor to perform a method according to any one of claims 1-5.

7. A non-transitory computer readable medium having computer readable instructions stored thereon for execution by a processor to perform the method according to any of claims 1-5.

8. The non-transitory computer readable medium of claim 7 comprising at least one of: a hard disk, a Compact Disc, an optical storage device, a magnetic storage device, a Read Only Memory, a Programmable Read Only Memory, an Erasable Programmable Read Only Memory, an Electrically Erasable Programmable Read Only Memory and a Flash memory and a Solid State Drive.

9. A base station apparatus comprising a processor, a storage unit and a communications interface, wherein the processor unit, storage unit, and communications interface are configured to perform the method as claimed in any one of claims 1-5.